Elwakeel, E. | Search bioRxiv

bioRxiv2026

Context-specific genetic interaction mapping reveals combinatorial KRAS/FGFR dependence in pancreatic cancer

Oncogenic KRAS drives >90% of pancreatic ductal adenocarcinoma (PDAC), and the pan-RAS inhibitor daraxonrasib doubles overall survival, but adaptive and acquired resistance limit the depth and duration of responses. To systematically map combination vulnerabilities, we developed RAS+, a digenic CRISPR knockout library testing 10,296 pairwise perturbations across oncogenic signaling pathways, and screened 12 cell lines, revealing genotype- and lineage-specific interactions. In KRASmut PDAC, the most differentially effective gene pair was KRAS and FRS2, an adapter coupling FGFR signaling to RAS. Combined KRAS/pan-FGFR inhibition was synergistic and cytotoxic, eradicating tumor cells and overcoming resistance in vitro and deepening and prolonging regressions in vivo. Single-cell analysis identified cancer-associated fibroblasts (CAFs) as a primary source of FGF ligands in PDAC, and CAF conditioned media or individual FGFs promoted resistance. These findings define a stroma-to-tumor adaptive FGFR circuit limiting the effects of KRAS inhibition. Thus, combined KRAS/FGFR blockade may be a strategy to improve responses.

cancer biology↗

bioRxiv2025

Machine Learning-Driven Drug Repurposing for KRAS G12C and KRAS G12D Inhibition

KRAS is a predominant oncogenic driver across multiple cancers, long considered "undruggable" due to its high nucleotide affinity and lack of classical binding pockets. Although recent advances have led to covalent inhibitors like Sotorasib and Adagrasib for the KRAS G12C mutation, effective therapies for other common variants--most notably KRAS G12D, which is highly prevalent in aggressive pancreatic cancers--remain limited. In this study, we employ machine learning to identify potential inhibitors for both KRAS G12D and G12C by screening FDA-approved compounds from the ChEMBL database. Random Forest and Neural Network models were trained on binding affinity data from three BindingDB datasets: wild-type KRAS GTPase, KRAS G12C, and KRAS G12D. Our models identified high-affinity candidates including anti-cancer kinase inhibitors (e.g., Cobimetinib, Gilteritinib) as well as drugs from other categories (e.g., Bromocriptine, Cefepime). By incorporating atomic hybridization as a feature, we aim to capture the effects of induced polarization, potentially improving prediction accuracy. Despite modest agreement across models, our approach highlights several promising candidates--such as Acalabrutinib, which has prior evidence of G12C activity--for further investigation. These results offer a data-driven foundation for experimental validation and the future development of targeted KRAS therapies.

cancer biology↗